积极维护。 以下是我如何将Claude代码用于学术工作——幻灯片、论文、数据分析等——的总结，方便你分叉用于自己的研究。请参见 CHANGELOG.md 了解最新变化。

直播网站： psantanna.com/claude-code-my-workflow

一个随时可分叉的AI辅助学术工作基础。你描述你想要的东西——讲义幻灯片、研究论文、数据分析、复制包——Claude负责规划方法，运行专业代理，修复问题，验证质量，并呈现结果。就像一个承包商负责整个工作。从生产型博士课程中提取，并由不断壮大的社区扩展。

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快速启动（5–10分钟，首次安装额外加~30分钟）

开始之前： Claude Code、git 和 Python 3 是最低要求。Python 3 运行钩子、门套件（./scripts/backtest.sh —— 10 个检查器）和质量评分器，并且预装在 macOS/Linux。要端到端运行附带的 HelloWorld 演示，还需要 XeLaTeX（Beamer 样本）和 Quarto（Quarto 样本）。推荐使用 R 和 GitHub CLI。完整列表见下面的 Prerequisites。最快路径：先克隆，然后运行 '。

My Claude Code Setup

Actively maintained. A summary of how I use Claude Code for academic work — slides, papers, data analysis, and more — packaged so you can fork it for your own research. See CHANGELOG.md for the latest changes.

Live site: psantanna.com/claude-code-my-workflow

A ready-to-fork foundation for AI-assisted academic work. You describe what you want — lecture slides, a research paper, a data analysis, a replication package — and Claude plans the approach, runs specialized agents, fixes issues, verifies quality, and presents results. Like a contractor who handles the entire job. Extracted from a production PhD course and extended by a growing community.

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Quick Start (5–10 minutes, plus ~30 min for first-time installs)

Before you start: Claude Code, git and Python 3 are the minimum. Python 3 runs the hooks, the gate suite (./scripts/backtest.sh — 10 checkers) and the quality scorer, and is pre-installed on macOS/Linux. To run the included HelloWorld demos end-to-end you also need XeLaTeX (Beamer sample) and Quarto (Quarto sample). R and the GitHub CLI are recommended. Full list in Prerequisites below. Fastest path: clone first, then run ./scripts/validate-setup.sh — it reports exactly what’s missing with install links.

Only need Python/R/markdown? You don’t need XeLaTeX or Quarto. The agents, rules, skills, and orchestration patterns work for any text/code artifact. Skip the HelloWorld demos and head straight to /data-analysis, /review-paper, /lit-review, or /review-r.

Session 2 onwards: MEMORY.md (committed) collects generic [LEARN] entries that help all forkers; machine-specific notes accumulate in Claude Code’s native auto memory (~/.claude/projects//memory/, machine-local, never committed). See .claude/rules/meta-governance.md for the distinction.

1. Fork & Clone

# Fork this repo on GitHub (click "Fork" on the repo page), then:
git clone https://github.com/YOUR_USERNAME/claude-code-my-workflow.git my-project
cd my-project
./scripts/validate-setup.sh        # reports missing tools with install links

Replace YOUR_USERNAME with your GitHub username.

2. Start Claude Code and Paste This Prompt

claude

Using VS Code? Open the Claude Code panel instead. Everything works the same — see the full guide for details.